• Molecules and Cells
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• v.37 no.8
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• pp.575-584
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• 2014

Potassium is a macronutrient that is crucial for healthy plant growth. Potassium availability, however, is often limited in agricultural fields and thus crop yields and quality are reduced. Therefore, improving the efficiency of potassium uptake and transport, as well as its utilization, in plants is important for agricultural sustainability. This review summarizes the current knowledge on the molecular mechanisms involved in potassium uptake and transport in plants, and the molecular response of plants to different levels of potassium availability. Based on this information, four strategies for improving potassium use efficiency in plants are proposed; 1) increased root volume, 2) increasing efficiency of potassium uptake from the soil and translocation in planta, 3) increasing mobility of potassium in soil, and 4) molecular breeding new varieties with greater potassium efficiency through marker assisted selection which will require identification and utilization of potassium associated quantitative trait loci.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Journal of the Korean Statistical Society
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• v.11 no.2
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• pp.109-117
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• 1982

In this paper, a preliminary study is performed on the subset selection procedures which are based on the trimmed means and the Hodges-Lehmann estimator derived from the Wilcoxon test. The proposed procedures are compared to the Gupta's rule through a small smaple Monte Carlo study. The results show that the procedures based on the robust estimators are successful in terms of efficiency and robustness.

• Korean Management Science Review
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• v.14 no.1
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• pp.77-85
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• 1997

This paper proposes an approach for individual selection in genetic algorithms to improve problem solving efficiency and effectiveness. To investigate the utility of combining simulated annealing with genetic algorithm, two experiment are conducted that compare both the conventional genetic algorithm and suggested approach. Result indicated that suggested approach significantly reduced the required time to find optimal solution in moderate-sized problems under the conditions studied. It is also found that quality of the solutions generated by suggested approach in large- sized problems is greatly improved.

• Journal of Korean Society for Quality Management
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• v.29 no.1
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• pp.11-23
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• 2001

This paper is concerned with suggesting a Bayesian method for variable selection in multinomial logit model. It is based upon an optimal rule suggested by use of Bayes rule which minimizes a risk induced by selecting the multinomial logit model. The rule is to find a subset of variables that maximizes the marginal likelihood of the model. We also propose a Laplace-Metropolis algorithm intended to suggest a simple method forestimating the marginal likelihood of the model. Based upon two examples, artificial data and empirical data examples, the Bayesian method is illustrated and its efficiency is examined.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.7
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• pp.889-897
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• 1999

In this paper, we propose a new method of path planning for autonomous mobile robot in mapped circumstance. To search the optimal path, we adopt the genetic algorithm which is based on the natural mechanics of selection, crossover and mutation. We propose a method for generating the path population, selection and evaluation in genetic algorithm. Simulations show the efficiency for the global path planning, if we adopt the proposed GA method

• Journal of the Korean Statistical Society
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• v.16 no.1
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• pp.26-36
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• 1987

In this paper, we study on a subset selection procedure based on Hodges-Lehmann estimators derived from the Wilcoxon test. To estimate the standard error of the Hodges-Lehmann estimators, the biweight A-estimator of scale is used. The Pitman efficiency of the proposed rule is compared with the Gupta's rule and the trimmed-means rule through a small-sample Monte Carlo study. The results show that the proposed rule satisfies the $P^*$-condition and is very efficient in various heavy-tailed distributions.

• Geomechanics and Engineering
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• v.16 no.2
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• pp.195-204
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• 2018

Based on theories of rock mechanics, rock fragmentation, mechanics of elasto-plasticity, and energy dissipation etc., a method is presented for evaluating the rock fragmentation efficiency by using plastic energy dissipation ratio as an index. Using the presented method, the fragmentation efficiency of rocks with different strengths (corresponding to soft, intermediately hard and hard ones) under indentation is analyzed and compared. The theoretical and numerical simulation analyses are then combined with experimental results to systematically reveal the fragmentation mechanism of rocks under indentation of indenter. The results indicate that the fragmentation efficiency of rocks is higher when the plastic energy dissipation ratio is lower, and hence the drilling efficiency is higher. For the rocks with higher hardness and brittleness, the plastic energy dissipation ratio of the rocks at crush is lower. For rocks with lower hardness and brittleness (such as sandstone), most of the work done by the indenter to the rocks is transferred to the elastic and plastic energy of the rocks. However, most of such work is transferred to the elastic energy when the hardness and the brittleness of the rocks are higher. The plastic deformation is small and little energy is dissipated for brittle crush, and the elastic energy is mainly transferred to the kinetic energy of the rock fragment. The plastic energy ratio is proved to produce more accurate assessment on the fragmentation efficiency of rocks, and the presented method can provide a theoretical basis for the optimization of drill bit and selection of well drilling as well as for the selection of the rock fragmentation ways.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.287-292
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• 2015

In this paper, we evaluated the performance of D2D advertisement dissemination algorithms with maximum-distance and transmission-efficiency based relay selections with respect to the total number of successfully received users and transmission efficiency. To assume more practical environment, we took into account pre-defined target-areas based on the information of user density and the limit for the maximum number of relay users. Through the simulations we compared the performance results of both D2D advertisement dissemination algorithms with maximum-distance and transmission-efficiency based relay selections according to increment of the number of sectors. And then, we analysed the superiority of algorithm with transmission-efficiency based relay selections more than maximum-distance based relay selections.

• Journal of Power Electronics
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• v.19 no.4
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• pp.922-933
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• 2019

The arm inductance (AI) of a modular multilevel converter (MMC) affects both the fault and circulating current magnitudes. In addition, it has an impact on the inverter efficiency and harmonic content. In this study, the AI of a three-phase MMC is optimized in a novel way in terms of DC voltage utilization, harmonics and efficiency. This MMC has 10 submodules (SM) per arm and the power circuit topology of the SM is a half-bridge. The optimum AI is adopted and verified in an MMC that has 100 SMs per arm. Then the phase shift (PS) and phase disposition (PD) pulse width modulation (PWM) methods are investigated for better DC voltage utilization, efficiency and harmonics. It is found that similar performances are obtained for both modulation techniques in terms of DC voltage utilization. However, the total harmonic distortion (THD) of the PS-PWM is found to be 0.02%, which is slightly lower than the THD of the PD-PWM at 0.16%. In efficiency calculations, the switching and conduction losses for all of the semiconductor are considered separately and the minimum efficiency of the 100-SM based MMC is found to be 99.62% for the PS-PWM and 99.64% for the PD-PWM with the optimal value of the AI. Simulation results are verified with an experimental prototype of a 6-SM based MMC.